code: def test_collect_callgrind(self):
      with self.assertRaisesRegex(
          ValueError,
          r"`collect_callgrind` requires that globals be wrapped "
          r"in `CopyIfCallgrind` so that serialization is explicit.",
      ):
          benchmark_utils.Timer(
              "pass", globals={"x": 1}
          ).collect_callgrind(collect_baseline=False)
  
      with self.assertRaisesRegex(
          # Subprocess raises AttributeError (from pickle),
          # _ValgrindWrapper re-raises as generic OSError.
          OSError,
          "AttributeError: Can't get attribute 'MyModule'",
      ):
          benchmark_utils.Timer(
              "model(1)",
              globals={
                  "model": benchmark_utils.CopyIfCallgrind(MyModule())
              },
          ).collect_callgrind(collect_baseline=False)
  
      @torch.jit.script
      def add_one(x):
          return x + 1
  
      timer = benchmark_utils.Timer(
          "y = add_one(x) + k",
          setup="x = torch.ones((1,))",
          globals={
              "add_one": benchmark_utils.CopyIfCallgrind(add_one),
              "k": benchmark_utils.CopyIfCallgrind(5),
              "model": benchmark_utils.CopyIfCallgrind(
                  MyModule(),
                  setup=f"""\
                      import sys
                      sys.path.append({repr(os.path.split(os.path.abspath(__file__))[0])})
                      from test_benchmark_utils import MyModule
                      """,
              ),
          },
      )
  
      stats = timer.collect_callgrind(number=1000)
      counts = stats.counts(denoise=False)
  
      self.assertIsInstance(counts, int)
      self.assertGreater(counts, 0)
  
      # There is some jitter with the allocator, so we use a simpler task to
      # test reproducibility.
      timer = benchmark_utils.Timer(
          "x += 1",
          setup="x = torch.ones((1,))",
      )
  
      stats = timer.collect_callgrind(number=1000, repeats=20)
      assert isinstance(stats, tuple)
  
      # Check that the repeats are at least somewhat repeatable. (within 10 instructions per iter)
      counts = collections.Counter(
          [s.counts(denoise=True) // 10_000 * 10_000 for s in stats]
      )
      self.assertGreater(
          max(counts.values()),
          1,
          f"Every instruction count total was unique: {counts}",
      )
  
      from torch.utils.benchmark.utils.valgrind_wrapper.timer_interface import (
          wrapper_singleton,
      )
  
      self.assertIsNone(
          wrapper_singleton()._bindings_module,
          "JIT'd bindings are only for back testing.",
      )
  
imports: import collections
  import json
  import os
  import re
  import textwrap
  import timeit
  from typing import Any, List, Tuple
  import unittest
  import torch
  import torch.utils.benchmark
  from torch.testing._internal.common_utils import TestCase, run_tests, IS_SANDCASTLE, IS_WINDOWS, slowTest, TEST_WITH_ASAN
  import expecttest
  import numpy
label_desc: Use image node_id 6 for calling the TestBenchmarkUtils obj's underlying member method code with example usage: obj.test_collect_callgrind() and returns: unknown